Abstract Significance: In this SBIR project, we propose to predict and detect pediatric severe sepsis by developing a machine-learning-based clinical decision support system for electronic health record (EHR) pediatric sepsis screening.Thepediatricpopulationisunderserved,withfundamentalresearchandunderstandingofpediatric sepsis syndromes lagging behind that of the adult population. The proposed work will develop machine learningsepsispredictionsonthehighlyheterogeneouspediatricpopulation,bycombiningmulti-tasklearning methodswithexpertclinicalknowledgeofhowpediatricsepsispresentationisdependentonageandonpre- existing conditions. The multi-task learning approach will use age or comorbidities to define ?tasks,? each one associated with prediction on a particular subpopulation, and then link the learning process together between tasks.ResearchQuestions:Whichmethodsofusingthesetask-definingparametersaremosteffective?How can we most effectively learn the degree of similarity between pediatric subpopulations and leverage this to improveclassificationperformance?PriorWork:InSightwasoriginallydevelopedtopredictsepsisandseptic shockfromadultEHRdata.Afterretrainingonpediatriccases,inpreliminaryexperimentswitharetrospective set of pediatric (2-17 yr) inpatient encounters (n = 11,127;? 103 [0.9%] severely septic), at the University of California San Francisco (UCSF), InSight achieved an AUROC 0.912 and 0.727 for the detection and 4-hour pre-onset prediction of sepsis. This performance can be improved for better pediatric sepsis prediction. Specific Aims: To empirically evaluate different learning schemes using age with and without multi-task methods, within the UCSF pediatric severe sepsis data set (Aim 1). To exploit an expert-proposed network graph structure for comorbidity-described pediatric subpopulations that provides superior predictive performance over nave methods and graphs, both for the overall population and for underserved subpopulations (Aim 2). Methods: We propose to use multi-task methods that penalize deviations between classifiers on neighboring tasks and that iteratively learn the strength of these links. These methods will be comparedwithtotaltaskindependence,orpassingageintoclassifiertrainingasanordinaryinput.Criteriafor Success: Success will be shown by 4-hour pre-onset AUROC gains of 0.02 (overall population) and 0.03 for thepreviouslyweakestofthreeagesubpopulations(2-5,6-12,and13-17yrs;?4-hourpre-onsetprediction,p< 0.05,McNemar?stest,4-foldcross-validation).Thebeststructureformappingsimilaritiesbetweencomorbidity taskswillimprovetheoverallAUROCby0.03(p < 0.05) and by 0.07 for ? 2 comorbidity subpopulations of ? 100 patients (p<0.05).Outcome:TheseimprovementswillenableInSighttodeliverstrongsepsispredictive performanceacrossthewidelyheterogeneouspediatricpopulation.